Model selection for physiology

Physiological response parameters were assessed using mixed-effects linear models across species and treatments. Model selection was carried out using backward elimination of random-effects followed by fixed-effects using the package lmerTest (version 3.1.3)

Protein

While value ~ species + fpco2 + ftemp + (1 | colony) + species:ftemp was the best-fit model structure identified, we wanted to model both pCO2 and temperature responses by species so moving forward we are using the following model structure:

value ~ species * (fpco2 + ftemp) + (1 | colony)


Figure:

Carbohydrate

While value ~ species + ftemp was the best-fit model structure identified, we wanted to model responses with a random effect of colony so moving forward we are using the following model structure:

value ~ species * (fpco2 + ftemp) + (1 | colony)


Figure:

Lipid

While value ~ species + ftemp + reef + species:ftemp + species:reef was the best-fit model structure identified, we wanted to model both pCO2 and temperature responses by species and with random effect of colony so moving forward we are using the following model structure:

value ~ species * (fpco2 + ftemp) + reef + species:reef + (1 | colony)


Figure:

Density

While value ~ species + fpco2 + ftemp + reef + (1 | colony) + species:fpco2 + species:ftemp + fpco2:ftemp + species:reef + fpco2:reef + ftemp:reef + species:fpco2:ftemp + species:fpco2:reef + species:ftemp:reef + fpco2:ftemp:reef + species:fpco2:ftemp:reef was the best-fit model structure identified, we wanted to model both pCO2 and temperature responses by species and with random effect of colony so moving forward we are using the following model structure:

value ~ species * (fpco2 + ftemp) + reef + species:reef + (1 | colony)


Figure:

Chlorophyll

Since the best-fit model fits our design, we will proceed with the following model structure:

value ~ species + fpco2 + ftemp + reef + (1 | colony) + species:fpco2 + species:ftemp + fpco2:ftemp + species:reef


Figure:

Total Host

Since the best-fit model fits our design, we will proceed with the following model structure:

value ~ species + fpco2 + ftemp + reef + (1 | colony) + species:fpco2 + species:ftemp + species:reef + fpco2:reef + species:fpco2:reef


Figure:

Calcification

This is the same model from Bove et al 2019, just matching aesthetics for this manuscript.

Figure 1


Figure 1. Modeled 95% confidence interval of (A) total host energy reserves (mg cm-2), (B) cell density (106 cells cm-2), and (C) Chlorophyll a (ug cm-2) for S. siderea, P. strigosa, and P. astreoides at T0 (green) or T90 (red/blue), with individual coral fragment physiology denoted by points. Blue denotes 28°C and red denotes 31°C, with pCO2 treatment along the x axis.


Correlation assessments

Here, I am exploring the relationships between each physiology parameter measured above.


Figure 2. Correlation matrix for S. siderea, P. strigosa, and P. astreoides depicting pair-wise comparisons of physiological parameters within each species. Colour and ellipse width denote R2 of each significant comparison, and blank grids represent non-significant pair-wise comparisons (P > 0.05).Each parameter is denoted in blue text along the diagonal of each plot. Correlations with R2 above 0.5 (shown in orange and red in matrix plot) are explored further below.


Siderastrea siderea


Pseudodiploria strigosa


Porites astreoides


Holobiont PCAs

Siderastrea siderea

## 
## Call:
## adonis(formula = value ~ reef + ftemp + fpco2, data = s_df_l,      method = "eu") 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##            Df SumsOfSqs MeanSqs F.Model      R2 Pr(>F)  
## reef        1       544   544.0  0.3565 0.00068  0.553  
## ftemp       1      3538  3537.7  2.3184 0.00443  0.146  
## fpco2       3     15517  5172.4  3.3897 0.01945  0.015 *
## Residuals 510    778211  1525.9         0.97543         
## Total     515    797810                 1.00000         
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Pseudodiploria strigosa

## 
## Call:
## adonis(formula = value ~ reef + ftemp + fpco2, data = p_df_l,      method = "eu") 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##            Df SumsOfSqs MeanSqs F.Model      R2 Pr(>F)  
## reef        1     10809 10809.2  3.4558 0.00709  0.067 .
## ftemp       1     18776 18775.5  6.0027 0.01231  0.012 *
## fpco2       3     31998 10666.2  3.4100 0.02098  0.021 *
## Residuals 468   1463839  3127.9         0.95963         
## Total     473   1525422                 1.00000         
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Porites astreoides

## 
## Call:
## adonis(formula = value ~ reef + ftemp + fpco2, data = a_df_l,      method = "eu") 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##            Df SumsOfSqs MeanSqs F.Model      R2 Pr(>F)  
## reef        1        78    78.2  0.0742 0.00018  0.789  
## ftemp       1      5023  5023.4  4.7659 0.01140  0.023 *
## fpco2       3     11786  3928.7  3.7273 0.02675  0.012 *
## Residuals 402    423715  1054.0         0.96167         
## Total     407    440603                 1.00000         
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Figure 2

Plasticity analyses

Questions to ask:

  • What should I use as a loading in my PCA?
  • Calculating distance from T0/control mean or from colony value?
  • What about duplicate samples from same colony per treatment or from same sample?
  • What about samples that don’t have a T0 or control fragment? Should I be comparing within colony or within T0/control?
  • I think I am going to model the responses so that colony is being considered. Thoughts?


Control Comparison (400 \(\mu\)atm; 28 °C)

S. siderea

aov(distance ~ pCO2 * temperature * reef)

##                  Df Sum Sq Mean Sq F value   Pr(>F)    
## fpco2             3  52.34  17.447  12.398 1.36e-06 ***
## ftemp             1   4.62   4.620   3.283 0.074304 .  
## reef              1  17.19  17.194  12.218 0.000826 ***
## fpco2:ftemp       3  19.90   6.635   4.715 0.004696 ** 
## fpco2:reef        3  10.08   3.361   2.388 0.076194 .  
## ftemp:reef        1   0.68   0.679   0.482 0.489638    
## fpco2:ftemp:reef  3   4.93   1.643   1.168 0.328330    
## Residuals        70  98.51   1.407                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Reef environment:

##           diff       lwr       upr        p adj
## N-F -0.8918785 -1.402693 -0.381064 0.0008610358

Temperature:

##            diff         lwr       upr      p adj
## 31-28 0.4629949 -0.04781959 0.9738094 0.07494486

pCO2:

##                 diff         lwr         upr        p adj
## 420-300  -1.04810665 -2.05630773 -0.03990557 3.852069e-02
## 680-300  -0.09048749 -1.08908913  0.90811415 9.951844e-01
## 3300-300  1.07998914  0.09026878  2.06970950 2.705280e-02
## 680-420   0.95761916  0.02655061  1.88868771 4.147164e-02
## 3300-420  2.12809579  1.20655914  3.04963244 3.394696e-07
## 3300-680  1.17047663  0.25945215  2.08150111 6.360085e-03




P. strigosa

aov(distance ~ pCO2 + temperature + reef + pCO2:temperature)

##             Df Sum Sq Mean Sq F value Pr(>F)  
## fpco2        3  11.91   3.971   2.066 0.1126  
## ftemp        1   3.54   3.539   1.841 0.1792  
## reef         1   3.00   2.999   1.560 0.2158  
## fpco2:ftemp  3  22.06   7.353   3.825 0.0135 *
## Residuals   70 134.57   1.922                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Reef environment:

##          diff        lwr      upr     p adj
## N-F 0.3966685 -0.2371093 1.030446 0.2160887

Temperature:

##            diff        lwr      upr     p adj
## 31-28 0.4365546 -0.2089166 1.082026 0.1817144

pCO2:

##               diff        lwr        upr      p adj
## 420-300  -1.268832 -2.6342004 0.09653637 0.07787008
## 680-300  -0.186533 -1.2666855 0.89361956 0.96853921
## 3300-300 -0.318319 -1.3726392 0.73600121 0.85665896
## 680-420   1.082299 -0.3197300 2.48432814 0.18635502
## 3300-420  0.950513 -0.4317124 2.33273847 0.27742695
## 3300-680 -0.131786 -1.2331696 0.96959758 0.98909261




P. astreoides

aov(distance ~ pCO2 + temperature + reef)

##             Df Sum Sq Mean Sq F value   Pr(>F)    
## fpco2        3  32.12  10.707   7.776 0.000173 ***
## ftemp        1   9.68   9.676   7.027 0.010177 *  
## reef         1   1.35   1.353   0.983 0.325427    
## Residuals   62  85.37   1.377                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Reef environment:

##          diff        lwr       upr     p adj
## N-F 0.2780969 -0.2947748 0.8509686 0.3356237

Temperature:

##            diff       lwr      upr      p adj
## 31-28 0.7811527 0.1859322 1.376373 0.01094389

pCO2:

##                 diff        lwr        upr        p adj
## 420-300  -1.36493076 -2.4275070 -0.3023545 0.0065068409
## 680-300   0.01915618 -1.0285582  1.0668705 0.9999591525
## 3300-300  0.48643513 -0.5926162  1.5654864 0.6353107458
## 680-420   1.38408693  0.3363726  2.4318013 0.0048748389
## 3300-420  1.85136589  0.7723146  2.9304172 0.0001582493
## 3300-680  0.46727895 -0.5971405  1.5316984 0.6546745575





T0 Comparison

S. siderea

## 
## Call:
## adonis(formula = value ~ treat2, data = s_plast_l, method = "eu") 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##            Df SumsOfSqs MeanSqs F.Model      R2 Pr(>F)  
## treat2      8     28005  3500.6  2.1277 0.03488  0.034 *
## Residuals 471    774911  1645.2         0.96512         
## Total     479    802916                 1.00000         
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

##             Df Sum Sq Mean Sq F value   Pr(>F)    
## treat2       8  44.96   5.620   4.859 5.79e-05 ***
## reef         1   3.94   3.937   3.404   0.0685 .  
## Residuals   86  99.47   1.157                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1


P. strigosa

## 
## Call:
## adonis(formula = value ~ treat2, data = p_plast_l, method = "eu") 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##            Df SumsOfSqs MeanSqs F.Model      R2 Pr(>F)  
## treat2      8     65917  8239.6   2.384 0.04285  0.023 *
## Residuals 426   1472357  3456.2         0.95715         
## Total     434   1538274                 1.00000         
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

##             Df Sum Sq Mean Sq F value  Pr(>F)   
## treat2       8  31.61   3.951   3.491 0.00173 **
## reef         1   9.92   9.916   8.763 0.00408 **
## Residuals   77  87.13   1.132                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1


P. astreoides

## 
## Call:
## adonis(formula = value ~ treat2, data = a_plast_l, method = "eu") 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##            Df SumsOfSqs MeanSqs F.Model    R2 Pr(>F)   
## treat2      8     32081  4010.1  3.6221 0.069  0.002 **
## Residuals 391    432883  1107.1         0.931          
## Total     399    464964                 1.000          
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

##             Df Sum Sq Mean Sq F value   Pr(>F)    
## treat2       8  83.07  10.384   12.94 3.03e-11 ***
## reef         1   8.66   8.656   10.78   0.0016 ** 
## Residuals   70  56.19   0.803                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1


Supplemental Tables

Table 1. T90 modeled mean coral host protein content and 95% confidence intervals for each species
treatment N mean lower 95% upper 95%
(a) SSID
288_28 11 0.49 0.40 0.57
311_31 9 0.44 0.35 0.52
3285_28 12 0.45 0.37 0.53
3309_31 12 0.39 0.31 0.47
405_31 12 0.46 0.38 0.54
447_28 12 0.52 0.44 0.60
673_28 13 0.47 0.38 0.54
701_31 12 0.41 0.33 0.49
(b) PSTR
288_28 16 0.56 0.49 0.64
311_31 9 0.32 0.23 0.41
3285_28 16 0.42 0.34 0.50
3309_31 8 0.17 0.08 0.27
405_31 6 0.18 0.07 0.29
447_28 5 0.42 0.30 0.54
673_28 14 0.45 0.37 0.53
701_31 7 0.21 0.11 0.31
(c) PAST
288_28 11 0.24 0.15 0.33
311_31 6 0.19 0.08 0.29
3285_28 12 0.11 0.02 0.20
3309_31 4 0.06 -0.05 0.17
405_31 7 0.16 0.07 0.27
447_28 12 0.21 0.12 0.30
673_28 10 0.15 0.06 0.25
701_31 9 0.11 0.01 0.20
Table 2. T90 modeled mean coral host lipid content and 95% confidence intervals for each species
treatment N mean lower 95% upper 95%
(a) SSID
288_28 11 0.40 0.29 0.50
311_31 9 0.37 0.26 0.48
3285_28 12 0.37 0.27 0.47
3309_31 12 0.35 0.25 0.45
405_31 12 0.38 0.29 0.48
447_28 12 0.41 0.30 0.51
673_28 13 0.33 0.23 0.43
701_31 12 0.31 0.21 0.41
(b) PSTR
288_28 16 0.27 0.18 0.37
311_31 9 0.14 0.02 0.24
3285_28 15 0.22 0.12 0.32
3309_31 8 0.08 -0.04 0.19
405_31 5 0.12 -0.03 0.26
447_28 5 0.26 0.11 0.40
673_28 14 0.22 0.11 0.32
701_31 7 0.07 -0.04 0.20
(c) PAST
288_28 11 0.21 0.10 0.33
311_31 6 0.26 0.13 0.38
3285_28 12 0.13 0.01 0.24
3309_31 4 0.20 0.07 0.33
405_31 7 0.22 0.10 0.34
447_28 12 0.17 0.06 0.27
673_28 10 0.11 0.01 0.23
701_31 9 0.16 0.05 0.28
Table 3. T90 modeled mean coral host carbohydrate content and 95% confidence intervals for each species
treatment N mean lower 95% upper 95%
(a) SSID
288_28 11 1.15 0.95 1.35
311_31 8 0.82 0.61 1.02
3285_28 12 1.10 0.91 1.29
3309_31 12 0.77 0.60 0.96
405_31 12 0.75 0.57 0.94
447_28 12 1.08 0.90 1.26
673_28 13 1.27 1.10 1.45
701_31 12 0.94 0.75 1.11
(b) PSTR
288_28 16 0.77 0.60 0.93
311_31 9 0.50 0.30 0.68
3285_28 16 0.62 0.45 0.78
3309_31 8 0.34 0.15 0.55
405_31 6 0.40 0.16 0.65
447_28 5 0.67 0.41 0.92
673_28 14 0.56 0.37 0.75
701_31 7 0.29 0.07 0.51
(c) PAST
288_28 11 0.82 0.61 1.03
311_31 6 0.65 0.41 0.88
3285_28 12 0.58 0.38 0.78
3309_31 4 0.41 0.16 0.65
405_31 7 0.73 0.51 0.95
447_28 12 0.90 0.71 1.10
673_28 10 0.61 0.41 0.81
701_31 9 0.43 0.23 0.64
Table 4. T90 modeled mean algal endosymbiont cell density and 95% confidence intervals for each species
treatment N mean lower 95% upper 95%
(a) SSID
288_28 11 3.32 2.23 4.46
311_31 9 2.45 1.33 3.58
3285_28 12 2.04 0.97 3.07
3309_31 12 1.18 0.12 2.23
405_31 12 2.61 1.55 3.67
447_28 12 3.48 2.42 4.50
673_28 13 2.96 1.95 3.98
701_31 12 2.10 1.04 3.14
(b) PSTR
288_28 16 2.16 1.14 3.15
311_31 9 0.42 -0.77 1.60
3285_28 16 1.53 0.53 2.52
3309_31 8 -0.27 -1.48 0.89
405_31 6 0.45 -0.96 1.86
447_28 5 2.16 0.75 3.61
673_28 14 1.71 0.68 2.75
701_31 7 -0.09 -1.30 1.14
(c) PAST
288_28 11 7.29 6.13 8.48
311_31 6 6.42 5.02 7.74
3285_28 12 5.92 4.74 7.16
3309_31 4 4.86 3.51 6.15
405_31 6 5.51 4.22 6.83
447_28 12 6.43 5.28 7.57
673_28 10 5.09 3.84 6.35
701_31 8 4.19 2.87 5.45
Table 5. T90 modeled mean algal endosymbiont chlorophyll a content and 95% confichlace intervals for each species
treatment N mean lower 95% upper 95%
(a) SSID
288_28 11 111.62 79.83 142.93
311_31 9 105.32 71.66 138.94
3285_28 12 48.77 17.98 79.06
3309_31 12 32.88 2.52 62.85
405_31 12 78.09 46.98 108.90
447_28 12 155.02 123.15 185.60
673_28 13 83.80 54.26 112.09
701_31 12 82.13 52.64 112.24
(b) PSTR
288_28 16 186.56 157.46 214.70
311_31 9 120.87 86.12 156.21
3285_28 16 78.92 50.45 107.95
3309_31 8 -1.60 -37.38 34.54
405_31 6 26.31 -13.62 66.76
447_28 5 161.10 118.69 203.66
673_28 14 83.70 54.83 112.50
701_31 7 17.80 -17.46 55.99
(c) PAST
288_28 11 96.99 64.84 130.13
311_31 6 155.80 116.69 193.74
3285_28 12 15.60 -16.92 47.45
3309_31 4 61.57 19.84 101.90
405_31 7 52.42 15.21 88.19
447_28 12 64.58 33.39 95.25
673_28 10 33.03 -0.94 66.96
701_31 9 95.56 61.63 129.07
Table 6. Total coral host physiology at T90 modeled mean and 95% confidence intervals for each physiological parameter per species
treatment N mean lower 95% upper 95%
(a) SSID
288_28 11 2.02 1.62 2.42
311_31 8 1.62 1.21 2.03
3285_28 12 1.92 1.56 2.28
3309_31 12 1.53 1.18 1.88
405_31 12 1.58 1.21 1.95
447_28 12 2.03 1.66 2.40
673_28 13 2.06 1.71 2.42
701_31 12 1.66 1.29 2.01
(b) PSTR
288_28 16 1.59 1.25 1.93
311_31 9 0.96 0.58 1.33
3285_28 15 1.27 0.90 1.62
3309_31 8 0.60 0.22 0.98
405_31 5 0.71 0.17 1.27
447_28 5 1.38 0.83 1.95
673_28 14 1.23 0.86 1.62
701_31 7 0.56 0.16 0.97
(c) PAST
288_28 11 1.26 0.83 1.68
311_31 6 1.12 0.66 1.58
3285_28 12 0.86 0.42 1.30
3309_31 4 0.56 0.14 0.97
405_31 7 1.13 0.71 1.56
447_28 12 1.26 0.85 1.67
673_28 10 0.85 0.43 1.27
701_31 9 0.73 0.31 1.14
## Note: zip::zip() is deprecated, please use zip::zipr() instead


Session information

Session information from the last run date on 2020-11-17:

## R version 3.5.1 (2018-07-02)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS High Sierra 10.13.6
## 
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] wesanderson_0.3.6  RColorBrewer_1.1-2 gridGraphics_0.5-0
##  [4] corrplot_0.84      Hmisc_4.2-0        Formula_1.2-3     
##  [7] survival_2.44-1.1  magick_2.2         ggpubr_0.2.4      
## [10] magrittr_1.5       vroom_1.2.0        lmerTest_3.1-3    
## [13] lme4_1.1-21        Matrix_1.2-17      kableExtra_1.1.0  
## [16] ggfortify_0.4.7    cowplot_1.0.0      Rmisc_1.5         
## [19] shiny_1.4.0        vegan_2.5-5        lattice_0.20-41   
## [22] permute_0.9-5      forcats_0.4.0      stringr_1.4.0     
## [25] purrr_0.3.3        tibble_2.1.3       tidyverse_1.3.0   
## [28] plotly_4.9.0       openxlsx_4.1.2     tidyr_1.0.0       
## [31] ggbiplot_0.55      scales_1.1.0       plyr_1.8.5        
## [34] dplyr_0.8.3        ggplot2_3.2.1      readr_1.3.1       
## [37] knitr_1.25        
## 
## loaded via a namespace (and not attached):
##  [1] minqa_1.2.4         colorspace_1.4-1    ggsignif_0.6.0     
##  [4] ellipsis_0.3.0      htmlTable_1.13.1    base64enc_0.1-3    
##  [7] fs_1.3.1            rstudioapi_0.10     farver_2.0.1       
## [10] ggrepel_0.8.1       bit64_0.9-7         fansi_0.4.1        
## [13] lubridate_1.7.4     xml2_1.2.2          splines_3.5.1      
## [16] jsonlite_1.6        nloptr_1.2.1        broom_0.5.2        
## [19] cluster_2.1.0       dbplyr_1.4.2        compiler_3.5.1     
## [22] httr_1.4.1          backports_1.1.5     assertthat_0.2.1   
## [25] fastmap_1.0.1       lazyeval_0.2.2      cli_2.0.1          
## [28] later_1.0.0         acepack_1.4.1       htmltools_0.4.0    
## [31] tools_3.5.1         gtable_0.3.0        glue_1.3.1         
## [34] Rcpp_1.0.3          cellranger_1.1.0    vctrs_0.2.4        
## [37] nlme_3.1-140        xfun_0.8            rvest_0.3.5        
## [40] mime_0.7            lifecycle_0.2.0     MASS_7.3-51.4      
## [43] hms_0.5.3           promises_1.1.0      parallel_3.5.1     
## [46] yaml_2.2.0          gridExtra_2.3       rpart_4.1-15       
## [49] latticeExtra_0.6-28 stringi_1.4.5       highr_0.8          
## [52] checkmate_1.9.4     boot_1.3-23         zip_2.0.3          
## [55] rlang_0.4.5         pkgconfig_2.0.3     evaluate_0.14      
## [58] labeling_0.3        htmlwidgets_1.3     bit_1.1-14         
## [61] tidyselect_1.0.0    R6_2.4.1            generics_0.0.2     
## [64] DBI_1.0.0           pillar_1.4.3        haven_2.2.0        
## [67] foreign_0.8-71      withr_2.1.2         mgcv_1.8-28        
## [70] nnet_7.3-12         modelr_0.1.5        crayon_1.3.4       
## [73] rmarkdown_1.16      readxl_1.3.1        data.table_1.12.2  
## [76] reprex_0.3.0        digest_0.6.23       webshot_0.5.1      
## [79] xtable_1.8-4        httpuv_1.5.2        numDeriv_2016.8-1.1
## [82] munsell_0.5.0       viridisLite_0.3.0